electron_correlation_function_green_n_for_local_current
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@ -72,6 +72,7 @@ right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_
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right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
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self_energy, gamma = guan.self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00, h01, h_lead_to_center)
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green, gamma_right, gamma_left = guan.green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian)
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G_n = guan.electron_correlation_function_green_n_for_local_current(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian)
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# calculate density of states # Source code: https://py.guanjihuan.com/calculate_density_of_states
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total_dos = guan.total_density_of_states(fermi_energy, hamiltonian, broadening=0.01)
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@ -1,7 +1,7 @@
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[metadata]
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# replace with your username:
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name = guan
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version = 0.0.43
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version = 0.0.44
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author = guanjihuan
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author_email = guanjihuan@163.com
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description = An open source python package
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@ -3,6 +3,7 @@
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# calculate Green functions
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import numpy as np
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import guan
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def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
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if np.array(hamiltonian).shape==():
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@ -101,3 +102,9 @@ def green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamilto
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right_self_energy, left_self_energy, gamma_right, gamma_left = self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
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green = np.linalg.inv(fermi_energy*np.identity(dim)-center_hamiltonian-left_self_energy-right_self_energy)
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return green, gamma_right, gamma_left
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def electron_correlation_function_green_n_for_local_current(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
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right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
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green = guan.green_function(fermi_energy, center_hamiltonian, broadening=0, self_energy=left_self_energy+right_self_energy)
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G_n = np.imag(np.dot(np.dot(green, gamma_left), green.transpose().conj()))
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return G_n
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